01. Arrays and Linked Lists:

• Arrays:

  • Contiguous memory storage for elements of the same data type.

  • O(1) time complexity for accessing elements using indices.

  • Static size, making resizing cumbersome.

• Linked Lists:

  • Dynamic data structure where elements (nodes) are connected by pointers.

  • Efficient for insertions and deletions but slower for random access.

  • Singly linked lists and doubly linked lists variations.

02. Stacks and Queues:

• Stacks:

  • Last In, First Out (LIFO) data structure.

  • Common operations include push (addition) and pop (removal).

  • Used in parsing expressions, backtracking, and undo mechanisms.

• Queues:

  • First In, First Out (FIFO) data structure.

  • Enqueue (addition) and dequeue (removal) operations.

  • Utilized in task scheduling, breadth-first search, and caching.

03 Trees and Graphs:

• Trees:

  • Hierarchical data structure with a root node and branches.

  • Binary Trees, Binary Search Trees, AVL Trees, and Red-Black Trees.

  • Efficient for hierarchical relationships and searching.

• Graphs:

  • Collection of nodes (vertices) and edges.

  • Directed and undirected graphs.

  • Applications in network modeling, social networks, and pathfinding algorithms.

04 Hash Tables:

• Hash Functions:

  • Maps data to a fixed-size array, enabling efficient data retrieval.

  • Collision resolution methods (open addressing, chaining).

  • Used in dictionaries, caches, and database indexing.

Understanding these essential data structures is pivotal as they form the building blocks for solving a wide range of computational problems. The choice of the appropriate data structure depends on the specific requirements of the problem at hand, and a solid grasp of these structures enhances a programmer’s ability to design efficient algorithms and build scalable applications.